Analyzing reviews for rating prediction and item recommendation

doi:10.3969/j.issn.1000-5641.2015.03.010

Abstract

Abstract: Recommender systems are widely deployed in Web applications that need to predict the preferences of users to items. They are popular in helping users find movies, books, music, and products in general. In this work, we design a method for item recommendation based on a novel model that captures correlations between hidden aspects in reviews and numeric ratings. It is motivated by the observation that a user’s preference against an item is affected by different aspects discussed in reviews. Our method first explores topic modeling to discover hidden aspects from review text. Profiles are then created for users and items separately based on aspects discovered in their reviews. Finally, we utilize logistic regression to model the user item relationship and the rating is modeled as the similarity between user and item profiles. Experiments over real world reviews demonstrate the advantage of our proposal over state of the art solution.

Key words: recommendation；hidden aspect；LDA；regression model, review analysis

CLC Number:

TP391

GAO Yi-fan,YU Wen-zhe,CHAO Ping-fu,ZHENG Zhi-ling,ZHANG Rong. Analyzing reviews for rating prediction and item recommendation[J]. Journal of East China Normal University(Natural Sc, 2015, 2015(3): 80-90.

References

［1］RAJARAMAN A, ULLMAN J D. Mining of Massive Datasets［M］. London: Cambridge University Press, 2011.

［2］BLANCOFERNNDEZ Y, PAZOSARIAS J J, GILSOLLA A, et al. A flexible semantic inference methodology to reason about user preferences in knowledgebased recommender systems［J］. KnowledgeBased Systems, 2008, 21(4): 305320.

［3］MCAULEY J, LESKOVEC J. Hidden factors and hidden topics: understanding rating dimensions with review text［C］//Proceedings of the 7th ACM conference on Recommender systems. ACM, 2013: 165172.

［4］SARWAR B, KARYPIS G, KONSTAN J, et al. Itembased collaborative filtering recommendation algorithms［C］//Proceedings of the 10th international conference on World Wide Web. ACM, 2001: 285295.

［5］KOREN Y, BELL R, VOLINSKY C. Matrix factorization techniques for recommender systems［J］. Computer, 2009, 42(8): 3037.

［6］KOREN Y, BELL R. Advances in collaborative filtering［M］//KANTOR P B, RICCI F, ROKACH L, et al. Recommender Systems Handbook. New York: Springer, 2010: 145186.

［7］BRODY S, EIHADAD N. An unsupervised aspectsentiment model for online reviews［C］//Human Language Technologies: The 2010 Annual Conference of the North American Chapter of the Association for Computational Linguistics. Association for Computational Linguistics, 2010: 804812.

［8］JO Y, OH A H. Aspect and sentiment unification model for online review analysis［C］//Proceedings of the fourth ACM international conference on Web search and data mining. ACM, 2011: 815824.

［9］TITOV I, MCDONALD R. Modeling online reviews with multigrain topic models［C］//Proceedings of the 17th international conference on World Wide Web. ACM, 2008: 111120.

［10］TITOV I, MCDONALD R T. A Joint Model of Text and Aspect Ratings for Sentiment Summarization［C］//ACL, 2008(8): 308316.

［11］POPESCU A M, ETZIONI O. Extracting product features and opinions from reviews［M］//KAO A, POTEET S R. Natural Language Processing and Text Mining. London: Springer, 2007: 928.

［12］PANG B, LEE L. Opinion mining and sentiment analysis［J］. Foundations and Trends in Information Retrieval, 2008, 2(12): 1135.

［13］QU L, IFRIM G, WEIKUM G. The bagofopinions method for review rating prediction from sparse text patterns［C］//Proceedings of the 23rd International Conference on Computational Linguistics. Association for Computational Linguistics. 2010: 913921.

［14］GANU G, ELHADAD N, MARIAN A. Beyond the stars: Improving rating predictions using Review text content［C］//WebDB, 2009.

［15］BLEI D M, NG A Y, JORDAN M I. Latent dirichlet allocation［J］. The Journal of Machine Learning Research, 2003, 3: 9931022.

［16］BAMMANN K. Statistical models: theory and practice［J］. Biometrics, 2006(62): 943.

［17］BISHOP C M. Pattern recognition and machine learning［M］. New York: Springer, 2006.

［18］LEMIRE D, MACLACHLAN A. Slope one predictors for online ratingbased collaborative filtering［C］//SDM, 2005, 5: 15.

［19］GANTNER Z, RENDLE S, FREUDENTHALER C, et al. MyMediaLite: A free recommender system library［C］//Proceedings of the fifth ACM conference on Recommender systems. ACM, 2011: 305308.

［20］TSAPARAS P, NTOULAS A, TERZI E. Selecting a comprehensive set of reviews［C］//Proceedings of the 17th ACM SIGKDD international conference on Knowledge discovery and data mining. ACM, 2011: 168176.

[1]	WANG Wei, ZHENG Jun. Improved collaborative filtering algorithm based on usersimilarity [J]. Journal of East China Normal University(Natural Sc, 2016, 2016(3): 60-66.
[2]	QIN Bo,TANG Qiu-nan,WANG Mei-qi. Review on location privacy protection research [J]. Journal of East China Normal University(Natural Sc, 2015, 2015(5): 14-27.
[3]	JIANG Jun-wen,WANG Xiao-ling. Review on trajectory data compression [J]. Journal of East China Normal University(Natural Sc, 2015, 2015(5): 61-76.
[4]	XIONG Sheng-chao,WU Xia,PENG Zhi-yong. Finegrained privacypreserving framework while ensuring data usability in trajectory databases [J]. Journal of East China Normal University(Natural Sc, 2015, 2015(5): 96-103.
[5]	YUAN Pei-Sen, SHU Xin, SHA Chao-Feng, XU Huan-Liang. Research of large scale graph data management with in memory computing techniques [J]. Journal of East China Normal University(Natural Sc, 2014, 2014(5): 55-71.
[6]	JING Ya-peng, ZHENG Jun, HU Wen-xin. Belongingness of Chinese dialect speech recognition based on deep neural network [J]. Journal of East China Normal University(Natural Sc, 2014, 2014(1): 60-67.
[7]	JI Wen-di, WANG Xiao-ling, ZHOU Ao-ying. Techniques for estimating click-through rates of Web advertisements: A survey [J]. Journal of East China Normal University(Natural Sc, 2013, 2013(3): 1-14.
[8]	SONG Le-yi, XIONG Hui, ZHANG Rong. Towards the next generation of mobile recommender systems [J]. Journal of East China Normal University(Natural Sc, 2013, 2013(3): 37-45.
[9]	PENG Dun-lu, YAO Nan, XU Wen-jie. Strategy for online advertising based on fuzzily clustering Web pages [J]. Journal of East China Normal University(Natural Sc, 2013, 2013(3): 54-59，69.
[10]	SONG Le-yi, GONG Xue-qing, ZHANG Rong, LIU Peng. Online advertising systems and related technology evolution [J]. Journal of East China Normal University(Natural Sc, 2013, 2013(3): 106-117.
[11]	GU Xiao-qing, FU Wei, QI Gui-chao. Connecting reading and learning: design of the information model of e-Textbook [J]. Journal of East China Normal University(Natural Sc, 2012, 2012(2): 81-90.
[12]	WU Yong-he, GUO Wei, ZHU Zhi-ting. Current and future trends of international standards for e-Education technology： ISO/IEC JTC1 SC36， its 24th plenary, WG meetings and open forum [J]. Journal of East China Normal University(Natural Sc, 2011, 2011(6): 139-151.
[13]	XU Jian-qiang;YUAN Zhen-dong;LI Gao-ping;LIN Jing-yu. Feature Extraction Method for 12-Lead ECG Based on Wavelet Transform(Chinese) [J]. Journal of East China Normal University(Natural Sc, 2005, 2005(2): 27-32.
[14]	. Analyzing reviews for rating prediction and item recommendation [J]. Journal of East China Normal University(Natural Sc, 0, (): 80-.